Median filter for denoising MRI: Literature review

In this paper, we study Alzheimer’s disease, noise in MRI, the Median filter and state of the art in this domain. The disease of the century, Alzheimer’s disease, which characterized the loss of various abilities such: cognitive, thinking, remembering, reasoning and behavioral. One of the tools to recognize an early detection of Alzheimer’s disease is medical imaging and the most utilized is the Magnetic Resonance Imaging (MRI). It is a grey color image with intensity in the range 0-255. Medical image segmentation helps us to pull out precious knowledge from a large quantity of medical image data. For better image segmentation, further phases must be processed. Like, reducing noise from MRI. Gaussian noise and Salt and pepper noise are examples of noises present in images. There are many denoising techniques, like the filtering domain and especially the median filter that proves its effectiveness in reducing the Salt and pepper noise.

I. Introduction

Due to a large amount of collected data in the medical field about individual patients, it’s impossible for humans to analyze it manually. Here comes the use of computer-aided diagnosis and machine learning techniques that become a powerful tool nowadays. The most important challenges in the medical domain are the analysis of biomedical data or medical images, detection or diagnostic of certain diseases as well as the extraction of understandable knowledge and patterns from medical imaging or diagnosis data [1]. Such objects may be too complicated to be represented correctly by a simple equation [2]. Among the evolutive medical studies nowadays, we mention Alzheimer’s disease, the disease of the century. It is a progressive brain disorder involving the loss of cognitive and thinking functioning, remembering, reasoning, and behavioral abilities. It begins with memory loss and can affect person’s ability to carry out daily tasks. Medical imaging plays an essential role in diverse medical diagnosis processes and can be used to recognize an early detection of Alzheimer’s disease. Some frequently used ones include Positron Emission Tomography (PET)[3], Magnetic Resonance Imaging (MRI), Cerebra-spinal Fluid (CSF), Single-Photon Emission Computed Tomography (SPECT) and Computerized Tomography (CT scans)[4]. Medical image segmentation helps us to pull out precious knowledge from a large volume of medical image data. Gaining from clinical diagnosis and biomedical research, it has become a central question in the clinical field and image processing. It deals with categorizing the pixels of an²1² image, grouping each medical image into different regions with diverse characteristics and selects useful facts for future decisions. For better image segmentation, further phases must be processed in order to read medical images clearly and to extract important knowledge from this type of images like the exact stage of Alzheimer’s disease with MRI. Such as reducing or removing noise from MRI. This paper is organized as follows; Section 2 presents Alzheimer’s disease and denoising techniques. Section 3 discusses the state of the art of the Median filter: its standard version and its proposed improvements. Section 4 resume the comparison between the mentioned methods and finally, a conclusion in Section 5.